Automated assessment of organization mood

ABSTRACT

Methods for preventing the transmission of sensitive information to locations outside of a secure network by a person who has legitimate access to the sensitive information are described. In some embodiments, in order for an end user of a computing device to establish a secure connection with a secure network and access data stored on the secure network, a client application running on the computing device may be required by the secure network. The client application may monitor visual cues (e.g., facial expressions and gestures) associated with the end user, detect suspicious activity performed by the end user based on the visual cues, and in response to detecting suspicious activity may perform mitigating actions to prevent the transmission of sensitive information such as alerting human resources personnel or requiring authorization prior to sending information to locations outside of the secure network.

BACKGROUND

This disclosure relates to systems and methods for preventing thetransmission of sensitive or misleading information to locations outsideof a secure network.

Humans communicate using both verbal and non-verbal communication.Non-verbal communication may include hand gestures and facialexpressions. Both a person's speech (including the words spoken and thetone used when speaking the words) and their facial expressions may becaptured and analyzed to detect the person's emotions or mood. Emotionsmay refer to feelings experienced by a person over a short period oftime in response to a particular event (e.g., anger due to reading aparticular email). A person's emotions may include anger, fear, sadness,happiness, neutral, and surprise. Moods may refer to a general emotionalstate that is experienced by a person over a relatively longer period oftime than an event triggered emotion.

Facial expressions may provide cues to emotions or moods experienced bya person during a real-time conversation (e.g., during a videoconferencing session) or while the person is reading or composing anemail message or other form of written communication. Facial expressionrecognition systems may be used to identify a person or characteristicsof the person (e.g., the age and gender of the person), recognize facialexpressions performed by the person over time (e.g., by matchingselected facial features or expressions with images stored in a facialexpressions database), and determine (or infer) an emotional state ofthe person based on the facial expressions performed by the person overtime. A facial expression recognition system may detect expressionsassociated with facial features (e.g., eyes, eyebrows, nose, or mouth)and changes in facial feature expressions (e.g., changes in thegeometric relationships between the eyes and eyebrows or nose and mouth)using machine-learning based techniques. As movement of facial musclesthat lead to particular facial expressions may be involuntarily orunintentionally performed by a person in a particular emotional state,the particular facial expressions may be a reliable indicator of theperson's particular emotional state.

BRIEF SUMMARY

According to one aspect of the present disclosure, technology forpreventing the transmission of sensitive or misleading information tolocations outside of a network is disclosed.

One embodiment comprises a method comprising determining a baselinegroup mood classification associated with a plurality of people. Thebaseline group mood classification corresponds with a first time period.The method further comprises transmitting an electronic message to aplurality of target addresses associated with the plurality of peoplesubsequent to the first time period and prior to a second time periodand determining a group mood classification associated with theplurality of people. The group mood classification corresponds with thesecond time period subsequent to the first time period. The methodfurther comprises detecting that the group mood classification hasdeviated from the baseline group mood classification by more than athreshold amount and transmitting an alert in response to detecting thatthe group mood classification has deviated from the baseline group moodclassification by more than the threshold amount.

One embodiment comprises a system comprising a storage device and aprocessor in communication with the storage device. The storage devicestores a baseline group mood classification associated with a pluralityof people. The baseline group mood classification corresponds with afirst time period. The processor determines a group mood classificationassociated with the plurality of people. The group mood classificationcorresponds with a second time period subsequent to the first timeperiod. The processor detects that the group mood classification hasdeviated from the baseline group mood classification by more than athreshold amount and transmits an alert in response to detecting thatthe group mood classification has deviated from the baseline group moodclassification by more than the threshold amount.

One embodiment comprises a computer program product comprising acomputer readable storage medium having computer readable program codeembodied therewith. The computer readable program code configured todetermine a baseline group mood classification associated with aplurality of people. The baseline group mood classification correspondswith a first time period. The computer readable program code configuredto determine a group mood classification associated with the pluralityof people. The group mood classification corresponds with a second timeperiod subsequent to the first time period. The computer readableprogram code configured to detect that the group mood classification hasdeviated from the baseline group mood classification by more than athreshold amount and transmit an alert in response to detecting that thegroup mood classification has deviated from the baseline group moodclassification by more than the threshold amount.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter. The claimed subject matter is not limited to implementationsthat solve any or all disadvantages noted in the Background.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the present disclosure are illustrated by way of example andare not limited by the accompanying figures with like referencesindicating like elements.

FIG. 1 depicts one embodiment of a networked computing environment.

FIG. 2A depicts one embodiment of a mobile device running a clientapplication.

FIG. 2B depicts one embodiment of mobile device running a clientapplication utilizing a virtual keyboard.

FIG. 2C depicts one embodiment of an image captured from a front-facingcamera of a computing device.

FIG. 2D depicts one embodiment of an image captured from a front-facingcamera of a computing device.

FIG. 3A is a flowchart describing one embodiment of a process forpreventing the transmission of sensitive information outside of a securenetwork.

FIG. 3B is a flowchart describing an alternative embodiment of a processfor preventing the transmission of sensitive information outside of asecure network.

FIG. 3C is a flowchart describing one embodiment of a process foracquiring a malicious activity filter associated with an end user.

FIG. 4A is a flowchart describing one embodiment of a process forpreventing the transmission of false statements.

FIG. 4B is a flowchart describing an alternative embodiment of a processfor preventing the transmission of false statements.

FIG. 4C is a flowchart describing one embodiment of a process fordetermining whether a document includes a false statement.

FIG. 5A is a flowchart describing one embodiment of a process forpreventing the transmission of private information.

FIG. 5B is a flowchart describing one embodiment of a process forpreventing the transmission of private information.

FIG. 5C is a flowchart describing one embodiment of a process forinferring authorization to private information.

FIG. 6A is a flowchart describing one embodiment of a process forpreventing the transmission of sensitive information.

FIG. 6B is a flowchart describing one embodiment of a process forpreventing the transmission of sensitive information.

FIG. 6C is a flowchart describing one embodiment of a process fordetermining a group mood classification.

FIG. 7A is a flowchart describing one embodiment of a process fordetermining a mood of an organization and for detecting shifts in themood of the organization.

FIG. 7B is a flowchart describing one embodiment of a process fordetecting a group response to an electronic message.

FIG. 7C is a flowchart describing one embodiment of a process fortransmitting an electronic message based on reactions of a group ofpeople.

FIG. 8 depicts one embodiment of a mobile device.

DETAILED DESCRIPTION

As will be appreciated by one skilled in the art, aspects of the presentdisclosure may be illustrated and described herein in any of a number ofpatentable classes or context including any new and useful process,machine, manufacture, or composition of matter, or any new and usefulimprovement thereof. Accordingly, aspects of the present disclosure maybe implemented entirely hardware, entirely software (including firmware,resident software, micro-code, etc.) or combining software and hardwareimplementation that may all generally be referred to herein as a“circuit,” “module,” “component,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

Any combination of one or more computer readable media may be utilized.The computer readable media may be a computer readable signal medium ora computer readable storage medium. A computer readable storage mediummay be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, or semiconductor system, apparatus, or device,or any suitable combination of the foregoing. More specific examples (anon-exhaustive list) of the computer readable storage medium wouldinclude the following: a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an appropriateoptical fiber with a repeater, a portable compact disc read-only memory(CD-ROM), an optical storage device, a magnetic storage device, or anysuitable combination of the foregoing. In the context of this document,a computer readable storage medium may be any tangible medium that cancontain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable signal medium may be transmitted usingany appropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, CII, VB.NETor the like, conventional procedural programming languages, such as the“C” programming language, Visual Basic, Fortran 2003, Perl, Python,COBOL 2002, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Aspects of the present disclosure are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatuses(systems) and computer program products according to embodiments of thedisclosure. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable instruction executionapparatus, create a mechanism for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that when executed can direct a computer, otherprogrammable data processing apparatus, or other devices to function ina particular manner, such that the instructions when stored in thecomputer readable medium produce an article of manufacture includinginstructions which when executed, cause a computer to implement thefunction/act specified in the flowchart and/or block diagram block orblocks. The computer program instructions may also be loaded onto acomputer, other programmable instruction execution apparatus, or otherdevices to cause a series of operational steps to be performed on thecomputer, other programmable apparatuses or other devices to produce acomputer implemented process such that the instructions which execute onthe computer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

Technology is described for preventing the transmission of sensitiveinformation (e.g., confidential information or other information thathas significant value to an organization) to sources located outside ofa secure network by a person who has legitimate access to the sensitiveinformation. In some embodiments, in order for an end user of acomputing device (e.g., a mobile device such as a mobile phone or tabletcomputing device) to establish a secure connection with a secure networkand access data stored on the secure network, a client applicationrunning on the computing device may be required by the secure network.The client application may monitor visual cues (e.g., facial expressionsand hand gestures) associated with the end user, detect suspiciousactivity performed by the end user based on the visual cues, and inresponse to detecting suspicious activity may perform mitigating actionsto prevent the transmission of sensitive information. In some cases, themitigating action may comprise alerting human resources personnel and/orrequiring authorization prior to sending information to locationsoutside of the secure network (e.g., the transmission of an emailoriginating from the end user's account to an email address that isoutside of the secure network). The mitigating action taken may dependon a business value rating associated with a document (or informationcontained within the document) that has been requested by the end userto be transmitted.

In some embodiments, the sensitive information may comprise keywords orphrases associated with confidential or secret information. Thesensitive information may be embedded within various document sourcessuch as email messages, instant messages, invention disclosuredocuments, draft versions of financial statements being developed forpublic release, and new product development documents. In someembodiments, the determination of whether a document includes sensitiveinformation may be based on a business value rating associated with thedocument. More information about methods for assigning a business valuerating to a document can be found in U.S. patent application Ser. No.12/814,842, entitled “System and Method for Assigning a Business ValueRating to Documents in an Enterprise,” which is herein incorporated byreference in its entirety.

One issue with granting employees access to sensitive information isthat it can be damaging to a company if an end user of a secure network,who has legitimate access rights to the sensitive information, performsmalicious activities such as sending the sensitive information tounauthorized persons and/or transmitting falsehoods regarding thesensitive information from the secure network. Moreover, a bad actor mayhack or otherwise illegally gain access to an account of the end userand use the end user's credentials to gain access to confidentialinformation stored on the secure network and attempt to transmit theconfidential information to sources located outside of the securenetwork. Furthermore, it may be damaging to a company if misleading orfalse information regarding the organization is transmitted from anemail account associated with the organization. Thus, there is a need toprevent the malicious transmission of sensitive information ormisleading information to sources located outside of a secure network.

FIG. 1 depicts one embodiment of a networked computing environment 100in which the disclosed technology may be practiced. Networked computingenvironment 100 includes a plurality of computing devices interconnectedthrough one or more networks 180. The one or more networks 180 allow aparticular computing device to connect to and communicate with anothercomputing device. The depicted computing devices include mobile device120, mobile device 130, mobile device 140, and server 160. In someembodiments, the plurality of computing devices may include othercomputing devices not shown. A computing device may comprise variouscomputing devices such as a mobile phone, laptop computer, desktopcomputer, or tablet computer. In some embodiments, the plurality ofcomputing devices may include more than or less than the number ofcomputing devices shown in FIG. 1. The one or more networks 180 mayinclude a secure network such as an enterprise private network, anunsecure network such as a wireless open network, a local area network(LAN), a wide area network (WAN), and the Internet. Each network of theone or more networks 180 may include hubs, bridges, routers, switches,and wired transmission media such as a wired network or direct-wiredconnection.

A server, such as server 160, may allow a client to download information(e.g., text, audio, image, and video files) from the server or toperform a search query related to particular information stored on theserver. In some cases, server 160 may act as a mail server or a fileserver. In general, a “server” may include a hardware device that actsas the host in a client-server relationship or a software process thatshares a resource with or performs work for one or more clients.Communication between computing devices in a client-server relationshipmay be initiated by a client sending a request to the server asking foraccess to a particular resource or for particular work to be performed.The server may subsequently perform the actions requested and send aresponse back to the client.

One embodiment of server 160 includes a network interface 165, processor166, and memory 167, all in communication with each other. Networkinterface 165 allows server 160 to connect to one or more networks 180.Network interface 165 may include a wireless network interface, a modem,and/or a wired network interface. Processor 166 allows server 160 toexecute computer readable instructions stored in memory 167 in order toperform processes discussed herein. In some cases, the server 160 mayestablish a secure connection with one or more computing devices (e.g.,using a virtual private network connection). Processor 166 may compriseone or more processing elements (e.g., multiple CPUs). In oneembodiment, server 160 may comprise a server for facilitating a livevideo conference.

One embodiment of mobile device 140 includes a network interface 145,processor 146, memory 147, camera 148, sensors 149, and display 150, allin communication with each other. Network interface 145 allows mobiledevice 140 to connect to one or more networks 180. Network interface 145may include a wireless network interface, a modem, and/or a wirednetwork interface. Processor 146 allows mobile device 140 to executecomputer readable instructions stored in memory 147 in order to performprocesses discussed herein. Camera 148 may capture images or video.Sensors 149 may generate motion and/or orientation informationassociated with mobile device 140. Sensors 149 may comprise an inertialmeasurement unit (IMU). Display 150 may display digital images and/orvideos. Display 150 may comprise a touchscreen display.

In some embodiments, various components of mobile device 140 includingthe network interface 145, processor 146, memory 147, camera 148, andsensors 149 may be integrated on a single chip substrate. In oneexample, the network interface 145, processor 146, memory 147, camera148, and sensors 149 may be integrated as a system on a chip (SOC). Inother embodiments, the network interface 145, processor 146, memory 147,camera 148, and sensors 149 may be integrated within a single package.

In some embodiments, mobile device 140 may provide a natural userinterface (NUI) by employing camera 148, sensors 149, and gesturerecognition software running on processor 146. With a natural userinterface, a person's body parts and movements may be detected,interpreted, and used to control various aspects of a computingapplication. In one example, a computing device utilizing a natural userinterface may infer the intent of a person interacting with thecomputing device (e.g., that the end user has performed a particulargesture in order to control the computing device).

Networked computing environment 100 may provide a cloud computingenvironment for one or more computing devices. Cloud computing refers toInternet-based computing, wherein shared resources, software, and/orinformation are provided to one or more computing devices on-demand viathe Internet (or other global network). The term “cloud” is used as ametaphor for the Internet, based on the cloud drawings used in computernetworking diagrams to depict the Internet as an abstraction of theunderlying infrastructure it represents.

In some embodiments, a mobile device, such as mobile device 140, may bein communication with a server in the cloud, such as server 160, and mayprovide to the server authentication information (e.g., a passwordassociated with an end user of the mobile device) and/or useridentification information (e.g., an alphanumeric user identifier)associated with the end user. In response, the server may transmit tothe mobile device security protected data accessible by the end user. Inone embodiment, the authentication information may be automaticallydetermined by the mobile device based on biometric characteristics ofthe end user. In another embodiment, the authentication information maybe automatically determined by the mobile device based on theidentification of various biometric characteristics of the end user, aswell as the detection of various gestures performed by the end user, andother factors such as the location of the mobile device.

In some embodiments, networked computing environment 100 may provideremote access to secure documents and applications to employees of acompany (or members of an organization) in order to allow them to workwithout being physically present at a company location (e.g., to enablean employee to work from home or while traveling). To facilitate remoteaccess to the secure documents and applications, a secure networkconnection may be established using a virtual private network (VPN). AVPN connection may allow an employee to securely access or transmit datafrom a private network (e.g., from a company file server or mail server)using an unsecure public network or the Internet. The VPN connectiontypically requires client-side software (e.g., running on the employee'sremote computing device) to establish and maintain the VPN connection.The VPN client software may provide data encryption and encapsulationprior to the transmission of secure private network traffic through theInternet.

In some embodiments, sensitive information may be stored withinelectronic files stored on or being sent from a server, such as server160. The electronic files may include, for example, word processingdocuments, spreadsheets, temporary documents, draft documents, draftemails, sent and/or received emails, instant messages, and textmessages. The electronic files may also be associated with metadata orinformation related to the electronic file such as the creator of thefile, the person to last edit the file, when the file was last updated,and groups or individuals associated with the file. In some cases, theelectronic files may be associated with a business value rating that isautomatically determined based on the presence of keywords (e.g.,important project names or employee names) or the creator of theelectronic file (e.g., an executive of a company).

FIG. 2A depicts one embodiment of mobile device 140 of FIG. 1 running aclient application. As depicted, mobile device 140 includes atouchscreen display 256, physical control buttons 254, a microphone 255,and a front-facing camera 253. The touchscreen display 256 may includean LCD display for presenting a user interface to an end user of themobile device. The touchscreen display 256 may include a status area 252which provides information regarding signal strength, time, and batterylife associated with the mobile device. Status area 252 may also provideinformation about an authentication level of the mobile device such aswhether a particular identification has been accepted. In someembodiments, the determination of the authentication level may be basedon a number of different biometric identifiers used for identifying anend user of the mobile device and/or a particular location of the mobiledevice (e.g., the mobile device may be located at the end user's home,office, or other frequently visited or predefined location associatedwith the end user). The microphone 255 may capture audio associated withthe end user (e.g., the end user's voice) for determining the identityof the end user and for detecting particular words spoken by the enduser. The front-facing camera 253 may be used to capture images of theend user for determining the identity of the end user and for detectingfacial expressions performed by the end user.

In one embodiment, the client application may comprise a computingapplication for establishing a secure connection to a secure network.The client application may require a user identifier to be entered intothe User ID field 272 and a corresponding password to be entered intothe Password field 273. The Log On button 274 may allow an end user ofmobile device 140 to submit the user credentials for establishing thesecure connection (e.g., to establish a VPN connection). In some cases,the client application may require biometric identification of the enduser of the mobile device. In one example, the client application mayrequire an identification of the end user via facial recognition basedon images captured by the front-facing camera 253.

In some embodiments, the client application may require continuousbiometric identification (e.g., facial recognition) of the end user ofthe mobile device while a secure connection is established. Thecontinuous identification of the end user may be used to detect when aperson different from the end user (e.g., a child or stranger) isoperating the mobile device while the secure connection is established,in which case the client application may close or terminate the secureconnection.

FIG. 2B depicts one embodiment of mobile device 140 of FIG. 1 running aclient application and utilizing a virtual keyboard 258 for data entry.The virtual keyboard 258 may be invoked automatically by the clientapplication or by selection of a particular entry field of the clientapplication by an end user of the mobile device. As depicted, an enduser of the mobile device 140 has drafted or edited an email message 259intended to be sent to a person associated with the email address 257(i.e., “tanya123@outsidemynetwork.com”). The email address of theintended recipient may be associated with a destination server that islocated outside of a secure network. The email message 259 includessensitive information including the code name of a secret project (i.e.,“Phoenix”), the name of a key employee of a company (i.e., “Jim Smith”),and a personal phone number associated with the key employee (i.e.,“555-0123”). In one embodiment, upon the detection of a suspiciousactivity performed by the end user of the mobile device 140, the emailmessage 259 may be analyzed for the presence of sensitive information,such as words associated with secret projects or personal contactinformation. If an email message is deemed to include sensitiveinformation, then the email message may be held in a buffer and itstransmission to the intended recipient may be delayed until a mitigatingaction has been performed (e.g., the email has been screened andapproved by a manager or human resources personnel).

In some embodiments, prior to transmission of an email message to anintended destination address, the email message may be scanned forsensitive information and if sensitive information has been identifiedwithin the email message, then audio and/or video captured from andbuffered on the mobile device 140 (e.g., captured data from the twominutes previous to the end user of the mobile device hitting the sendbutton to send the email message) may be analyzed in order to detectmalicious or suspicious activity (e.g., the end user projecting anger orfrustration) performed by the end user of the mobile device. In theevent that a malicious or suspicious activity was detected within thebuffered audio and/or video, the client application may inform a secureserver of the detected activity and the secure server may perform amitigating action prior to sending the email message to the intendedrecipient.

In some cases, if the client application determines that the end userhas performed a suspicious activity, then other contextual informationassociated with the end user may be acquired such as a degree ofactivity associated with the end user's network account (e.g., theamount of web traffic or outgoing/incoming data through a firewall of asecure network). Short term trends in the degree of activity may becalculated and compared with baseline values associated with the enduser's activities over time. In one example, a ratio of short-term datadownloads to long-term data downloads may be used to identify periodswhere the end user is downloading more information from a secure networkthan is typical for the end user. The contextual information associatedwith the end user may also include performance review metrics and humanresource metrics. The contextual information may be used to furtherprovide indication of suspicious activities performed by the end user(e.g., increased downloads above baseline conditions or having beenpreviously identified as a disgruntled employee by human resourcespersonnel).

FIG. 2C depicts one embodiment of an image captured from a front-facingcamera of a computing device, such as front-facing camera 253 in FIG.2A. As depicted, the image includes a representation of an end user 238of the computing device. The image may be analyzed in order to identifythe end user 238 using facial recognition techniques and to detectparticular facial expressions performed by the end user (e.g., smiling)using facial expression recognition techniques.

FIG. 2D depicts one embodiment of the image captured from a front-facingcamera of a computing device, such as front-facing camera 253 in FIG.2A. As depicted, the image may be analyzed in order to identify facialfeatures 232-234 associated with an end user of the computing device.The facial features 232-233 may correspond with eyes and eyebrows of theend user and facial feature 234 may correspond with the mouth of the enduser. Changes in facial expressions (e.g., eyes narrowing, changes inblinking patterns, and changes in the shape of the end user's eyebrows)or changes in facial characteristics (e.g., eye dilation, changes in thesize of the end user's iris, and the presence of sweat on the end user'sforehead) may be used to identify stress in the end user and to detectfacial expressions or other facial movements corresponding withsuspicious activities. In some cases, the facial expressions ormovements detected may be used to infer whether the end user is lying orperforming a malicious activity.

In one embodiment, a client application may determine a degree oftruthfulness based on an end user's facial expressions and movementswhile the end user is performing work-related tasks, such as readinginformation downloaded from a secure network or drafting an emailmessage to be sent from the secure network. The client application mayalso consider changes in typing posture, changes in typing speed, or thedetection of nervous or anxious movements as sensed using a motionsensor, such as sensors 149 in FIG. 1. For example, an end user'snervousness may be identified due to excessive finger shaking beyond abaseline level of finger shaking typically associated with the end userwhen the end user is controlling a touchscreen display or a virtualkeyboard, such as virtual keyboard 258 of FIG. 2B.

In one embodiment, a malicious activity filter including one or morerules for determining when an end user of a computing device isperforming suspicious activities while operating the computing devicemay be used by the client application. The one or more rules may includedetecting particular facial expressions or gestures performed by the enduser or detecting particular phrases expressing anger or frustrationspoken by the end user. The one or more rules for determining when theend user is performing suspicious activities may also take into accountthe time of day, the location of the end user, and the computing deviceused by the end user (e.g., a phone, laptop, or desktop computingdevice). In some cases, baseline moods associated with the end user maycorrespond with different times of the day and with different locations(e.g., a first baseline mood may be associated with an end useroperating a desktop computing device at work during the daytime and asecond baseline mood may be associated with the end user operating amobile device at home at night). Other baseline behaviors associatedwith the end user such as typical typing speeds, typical data downloads,and typical degrees of finger shaking may also be determined fordifferent times of the day, for different locations of the end user, andfor different computing devices used by the end user. The location ofthe computing device may be determined by acquiring GPS locationinformation associated with the computing device used by the end user.

FIG. 3A is a flowchart describing one embodiment of a process forpreventing the transmission of sensitive information outside of a securenetwork. In one embodiment, the process of FIG. 3A is performed by amobile device, such as mobile device 140 in FIG. 1.

In step 302, a secure connection is established with a network using acomputing device. The secure connection may comprise a VPN connection.The secure connection may provide remote access by the computing deviceto the network and allow for an end user of the computing device toaccess secure resources, files, and/or other information stored on thenetwork. In some cases, in order for the secure connection to beestablished and maintained, client monitoring software running on thecomputing device may be required.

In step 304, images of an end user of the computing device are captured.The images may be captured using a camera, such as front-facing camera253 in FIG. 2A. In some embodiments, video and/or audio associated withthe end user may be captured while the end user operates the computingdevice. The video and/or audio acquired may be used to monitor the enduser as the end user performs work-related tasks such as drafting emailsor editing spreadsheets. In step 306, an identification of the end useris determined based on the images. The identification of the end usermay be determined by applying facial recognition techniques to theimages. In one example, facial recognition techniques may be used toidentify the end user based on a database of employee images.

In some embodiments, the computing device may require continuousidentification of the end user while the secure connection isestablished. The continuous identification of the end user may be usedto detect when a person different from the end user (e.g., a child orstranger) is operating the computing device while the secure connectionis established, in which case the computing device may close orterminate the secure connection.

In step 308, a malicious activity filter associated with theidentification of the end user is acquired. In some cases, each employeeof a company (or member of an organization) may be associated with anindividualized malicious activity filter. The malicious activity filtermay comprise one or more rules for determining when the end user hasperformed a suspicious activity (e.g., an activity that requires aheightened degree of monitoring or precautions to be taken in order toprevent the transmission of sensitive information to sources outside ofa network). The one or more rules may include detecting particularfacial expressions or gestures performed by the end user and/ordetecting particular phrases expressing anger or frustration spoken bythe end user. The one or more rules for determining when the end user isperforming suspicious activities may also take into account the time ofday and the location of the end user. The location of the computingdevice may be determined by acquiring GPS location informationassociated with the computing device used by the end user.

In some embodiments, the malicious activity filter may be satisfied ifthe end user has displayed or expressed anger or frustration asdetermined by applying facial expression and mood detection techniquesto the images. In one example, if the end user is deemed to be in anangry, frightened, or anxious mood, then the malicious activity filtermay be satisfied. In another example, if the end user is deemed to beangry and they are on a watchlist for an organization (e.g., tagged as adisgruntled employee), then the malicious activity filter may besatisfied. In another embodiment, the malicious activity filter may besatisfied if the end user is deemed to be overly excited or overly happyas compared with a baseline emotional level. One embodiment of a processfor acquiring a malicious activity filter is described later inreference to FIG. 3C.

In step 310, it is detected that a suspicious event has occurred basedon the malicious activity filter as applied to the captured images. Thesuspicious event may be deemed to have occurred when the maliciousactivity filter has been satisfied or when a combination of one or morerules for determining that a suspicious activity has occurred has beensatisfied.

In some embodiments, facial expressions or movements performed by theend user may be used to infer whether the end user is lying orperforming a malicious activity. In one example, changes in facialexpressions (e.g., eyes narrowing, changes in blinking patterns, andchanges in the shape of the end user's eyebrows) or changes in facialcharacteristics (e.g., eye dilation, changes in the size of the enduser's iris, and the presence of sweat on the end user's forehead) maybe used to identify stress in the end user and to detect whether asuspicious event has occurred. The determination of whether a suspiciousevent has been detected may also take into account the end user's facialexpressions and movements while the end user is performing a particularwork-related task, such as reading information downloaded from a securenetwork or drafting an email message to be sent from the secure network.In some cases, a suspicious event may be detected when nervous oranxious movements performed by the end user are identified. For example,excessive finger shaking beyond a baseline level of finger shakingtypically associated with the end user when the end user is controllinga touchscreen display or a virtual keyboard, such as virtual keyboard258 of FIG. 2B, may trigger the detection of a suspicious event.

In step 312, it is determined whether the end user is editing (or hasrecently edited) a document associated with sensitive information. Inone embodiment, the document may comprise a draft email being edited,amended, or written by the end user. The document may be associated withsensitive information if any attachments to the document includekeywords or phrases associated with sensitive information or if thedocument and/or any attachments to the documents have metadatacorresponding with sensitive information. The metadata may identify adocument as containing confidential information. In another embodiment,the document may comprise a project related document includingconfidential information. The sensitive information may comprisekeywords or phrases associated with confidential or secret information.The sensitive information may be embedded within various documentsources such as emails or instant messages, invention disclosuredocuments, draft versions of financial statements being developed forpublic release, and new product development documents. In someembodiments, the determination of whether a document includes sensitiveinformation may be based on a business value rating associated with thedocument.

In some cases, it may be determined that the end user is viewing orreading a document associated with sensitive information based on imagescaptured of the end user using a front-facing camera, such asfront-facing camera 253 of FIG. 2A. In one embodiment, eye trackingtechniques may be used to determine if the end user is reading adocument. For example, the end user may be deemed to be reading thedocument if they are looking at a display displaying the document andtheir eye movements correspond with a tracking of words in the document.The triggering of mitigating actions may be performed in response to thedetecting of a suspicious event performed by the end user and detectingthat the end user is viewing or reading a document associated withsensitive information.

In step 314, data transmission buffering is enabled in response todetecting the suspicious event in step 310. The data transmissionbuffering may buffer or delay the transmission of any data from thecomputing device (or in the case that the document resides on and isbeing edited on a remote server, from the remote server) to anydestinations outside of the network. In some cases, a ten minute delaymay be used to allow for processing by human resources personnel (orother authorized company personnel such as the end user's manager) tothe contents of any data transmission in which a business value ratingor confidential information rating is above a particular threshold. Inother cases, an automated authorization system may be used to make athreshold determination of whether or not to permit transmission of thedocument to destinations outside of the network.

In step 316, it is determined that the end user intends to transmit thesensitive information outside of the network. The determination ofwhether the end user intends to transmit sensitive information outsideof the network may be performed in response to detecting the suspiciousevent in step 310. It may be determined that the end user intends totransmit the sensitive information (or a document associated with thesensitive information) outside of the network when the end user hasinitiated a data transfer for the sensitive information to a locationoutside of the network. In one embodiment, it may be determined that theend user intends to transmit sensitive information outside of thenetwork if the end user attempts to send an email message containing thesensitive information (e.g., the end user hit a send button associatedwith transmission of the email message). In another embodiment, it maybe determined that the end user intends to transmit sensitiveinformation outside of the network if the end user attempts to initiatea document transfer (e.g., using FTP) to destinations located outside ofthe network.

In step 318, a mitigating action is performed in response to determiningthat the end user intends to transmit the sensitive information outsideof the network. In one embodiment, the mitigating action may comprise analert issued to human resources personnel that requires authorization bythe human resources personnel before the sensitive information may betransmitted outside of the network. The mitigating action may includedelaying the data transmission for a period of time corresponding with abusiness value rating of the data to be transmitted (e.g., delaying theintended data transmission by ten minutes if the data includes the nameof a key employee or delaying the intended data transmission by 24 hoursif the data includes the code name of a secret project). In anotherembodiment, the mitigating action may comprise an alert issued to theend user of the computing device alerting them to the fact that theirintended data transmission may cause the transmission of sensitiveinformation to a destination located outside of the network. The enduser may then be required to confirm that they intend to make the datatransmission.

In one embodiment, a watermark or a hidden source identifier may beattached to documents in the intended data transmission in order toprovide a trail in the event that the sensitive information is leaked tosources outside of the network. The hidden source identifier maycorrespond with an email address of the end user or an employee numberassociated with the end user.

FIG. 3B is a flowchart describing an alternative embodiment of a processfor preventing the transmission of sensitive information outside of asecure network. In one embodiment, the process of FIG. 3B is performedby a mobile device, such as mobile device 140 in FIG. 1.

In step 322, an identification of an end user of a computing device isdetermined. The identification of the end user may be determined byapplying facial recognition techniques to one or more images captured bya front facing camera of the computing device. The one or more imagesmay be captured using a camera, such as front-facing camera 253 in FIG.2A. In one example, facial recognition techniques may determine theidentification of the end user based on a database of employee images.In some embodiments, video and/or audio associated with the end user maybe simultaneously captured while the end user operates the computingdevice. The video and/or audio acquired may be used to monitor the enduser as the end user operates the computing device. In some embodiments,the computing device may perform continuous identification of the enduser in order to detect when a person different from the end user (e.g.,a different employee) is operating the computing device.

In step 324, a malicious activity filter associated with theidentification of the end user is acquired. In some cases, each employeeof a company (or member of an organization) may be associated with anindividualized malicious activity filter. The malicious activity filtermay comprise one or more rules for determining when the end user hasperformed a suspicious activity (e.g., an activity that requires aheightened degree of monitoring or precautions to be taken in order toprevent the transmission of sensitive information to sources outside ofthe network). The one or more rules may include detecting particularfacial expressions or gestures performed by the end user or detectingparticular phrases expressing anger or frustration. The one or morerules for determining when the end user is performing suspiciousactivities may also take into account the time of day and the locationof the end user. The location of the computing device may be determinedby acquiring GPS location information associated with the computingdevice used by the end user.

In some embodiments, the malicious activity filter may be satisfied ifthe end user has displayed or expressed anger or frustration asdetermined by applying facial expression and mood detection techniquesto the images. In one example, if the end user is deemed to be in anangry, frightened, or anxious mood, then the malicious activity filtermay be satisfied. In another example, if the end user is deemed to beangry and they are on a watchlist for an organization (e.g., tagged as adisgruntled employee), then the malicious activity filter may besatisfied. One embodiment of a process for acquiring a maliciousactivity filter is described later in reference to FIG. 3C.

In step 326, it is determined that the end user is editing (or hasrecently edited) a document associated with sensitive information. Inone embodiment, the document may comprise a draft email being edited,amended, or written by the end user. The document may be associated withsensitive information if any attachments to the document includekeywords or phrases associated with sensitive information or if thedocument and/or any attachments to the documents have metadatacorresponding with sensitive information. The metadata may identify adocument as containing confidential information. The sensitiveinformation may comprise keywords or phrases associated withconfidential or secret information.

In one embodiment, each document that has been recently touched oredited by the end user (e.g., within the last ten minutes) may beanalyzed for sensitive information. For example, all draft emailsrecently edited by the end user may be analyzed. In some embodiments,the determination that a document is associated with sensitiveinformation may be based on a business value rating associated with thedocument.

In another embodiment, it may be determined that the end user is viewingor reading a document associated with sensitive information based onimages captured of the end user using a front-facing camera, such asfront-facing camera 253 of FIG. 2A. In one example, the end user may bedeemed to be reading the document if they are looking at a displaydisplaying the document and their eye movements correspond with atracking of words in the document. The triggering of mitigating actionsmay be performed in response to the detecting of a suspicious eventperformed by the end user and detecting that the end user is viewing orreading a document associated with sensitive information.

In step 328, images of an end user of the computing device are captured.The images may be captured in response to determining that the end useris editing a document associated with sensitive information. In thiscase, the monitoring of the end user for the performance of maliciousactivities may only be performed when the end user is working on,viewing, and/or controlling documents associated with sensitiveinformation. The document may be stored on the computing device or aremote server controlled by the computing device (e.g., in the case thatthe document resides on a remote server that has a secure connectionwith the computing device). The end user may edit the document stored onthe remote server using the computing device. The images may be capturedusing a camera, such as front-facing camera 253 in FIG. 2A.

In step 330, it is detected that a suspicious event has occurred basedon the malicious activity filter and the captured images. The suspiciousevent may be deemed to have occurred when the malicious activity filterhas been satisfied or when a combination of one or more rules fordetermining that a suspicious activity has occurred has been satisfied.

In some embodiments, facial expressions or movements performed by theend user may be used to infer whether the end user is lying orperforming a malicious activity. In one example, changes in facialexpressions (e.g., eyes narrowing, changes in blinking patterns, andchanges in the shape of the end user's eyebrows) or changes in facialcharacteristics (e.g., eye dilation, changes in the size of the enduser's iris, and the presence of sweat on the end user's forehead) maybe used to identify stress in the end user and to detect whether asuspicious event has occurred. The determination of whether a suspiciousevent has been detected may also take into account the end user's facialexpressions and movements while the end user is performing work-relatedtasks such as reading information downloaded from a secure network ordrafting an email message. In some cases, a suspicious event may bedetected when nervous or anxious movements performed by the end user areidentified. For example, excessive hand shaking beyond a baseline levelof hand shaking typically associated with the end user when the end useris controlling a touchscreen display or a virtual keyboard, such asvirtual keyboard 258 of FIG. 2B, may trigger the detection of asuspicious event.

In some embodiments, a suspicious event may be detected if more than onemalicious activity filters associated with different individuals with aparticular degree of closeness are all satisfied within a short periodof time. The degree of closeness may correspond with a social graph orsocial networking graph. The social graph may be associated with aparticular social networking service such as Facebook, LinkedIn, orTwitter. The degree of closeness may also correspond with whether thedifferent individuals are part of a common group or organization (e.g.,the individuals work on the same project team or for the same divisionwithin a company). In some cases, a density of suspicious activities(i.e., the number of suspicious events detected within a period of time)may be used to detect malicious collaboration between a plurality ofindividuals. In one example, a suspicious event may be detected if twoor more individuals of a group who are socially connected via a degreeof closeness perform suspicious activities within a particular period oftime (e.g., within a ten minute period). In some embodiments, asuspicious event may be triggered based on activities performed by theplurality of individuals that would otherwise not be triggered by onlythe activities of one of the plurality of individuals.

In step 332, data transmission buffering is enabled in response todetecting the suspicious event in step 330. The data transmissionbuffering may buffer or delay the transmission of any data from thecomputing device to any destinations outside of the network. The datatransmission buffering may buffer or delay the transmission of any datato any destinations outside of the network in the case that the documentresides on a remote server of the network that has a secure connectionwith the computing device.

In some cases, a ten minute delay may be used to allow for processing byhuman resources personnel (or other authorized company personnel such asthe end user's manager) to the contents of any data transmission inwhich a business value rating or confidential information rating isabove a particular threshold. In other cases, an automated authorizationsystem may be used to make a threshold determination of whether or notto permit transmission of the document to destinations outside of thenetwork.

In step 334, it is determined that the end user intends to transmit thesensitive information outside of the network. The determination ofwhether the end user intends to transmit sensitive information outsideof the network may be performed in response to detecting the suspiciousevent in step 330. In one embodiment, it may be determined that the enduser intends to transmit sensitive information outside of the network ifthe end user attempts to send an email message containing the sensitiveinformation (e.g., the end user hit a send button associated withtransmission of the email message). In another embodiment, it may bedetermined that the end user intends to transmit sensitive informationoutside of the network if the end user attempts to initiate a documenttransfer (e.g., using FTP) to sources located outside of the network.

In step 336, a mitigating action is performed in response to determiningthat the end user intends to transmit the sensitive information outsideof the network. In one embodiment, the mitigating action may comprise analert issued to human resources personnel that requires authorization bythe human resources personnel before the sensitive information may betransmitted outside of the network. The mitigating action may includedelaying the data transmission for a period of time corresponding with abusiness value rating of the data to be transmitted (e.g., delaying theintended data transmission by ten minutes if the data includes the nameof a key employee or delaying the intended data transmission by 24 hoursif the data includes the code name of a secret project). In anotherembodiment, the mitigating action may comprise an alert issued to theend user of the computing device alerting them to the fact that theirintended data transmission may cause the transmission of sensitiveinformation to sources located outside of the network. The end user maythen be required to confirm that they intend to make the datatransmission.

FIG. 3C is a flowchart describing one embodiment of a process foracquiring a malicious activity filter associated with an end user. Theprocess described in FIG. 3C is one example of a process forimplementing step 308 in FIG. 3A or for implementing step 324 in FIG.3B. In one embodiment, the process of FIG. 3C is performed by a mobiledevice, such as mobile device 140 in FIG. 1.

In step 362, an identification of an end user is acquired. Theidentification may comprise, for example, a user name or employeenumber. In step 364, a suspicion level associated with the end user isacquired. In one embodiment, the suspicion level may be set based onwhether the end user is on a watchlist associated with an organizationor has otherwise been tagged as a person of interest by theorganization.

In step 366, an individual mood classification associated with a mood ofthe end user is determined. The mood of the end user may be determinedover a period of time (e.g., within a four hour period or over a 24-hourperiod). The individual mood classification may be determined byapplying facial expression and mood detection techniques to capturedimages of the end user over the period of time. The individual moodclassification may classify a mood of the end user as angry, frustrated,sad, anxious, neutral, or happy.

In some cases, the individual mood classification may be determinedbased on baseline mood characteristics associated with typical end userfacial expressions that occur during different times of the day and/orwhen the end user is working in different locations (e.g., the number oftimes that the end user typically makes a sad face or displays anger maybe lower at night at home than during the day at work). Other baselinebehaviors associated with the end user such as typical degrees of finger(or hand) shaking during different times of the day or when in differentlocations may be taken into account when determining the individual moodclassification. The location of the end user may be determined byacquiring GPS location information associated with a mobile device usedby the end user (e.g., the end user's cell phone).

In one embodiment, images of the end user may be captured periodically(e.g., every second or every 30 seconds) while the end user is using acomputing device. In some cases, the images may be captured usingfront-facing cameras associated with multiple computing devices andaggregated during the course of a mood sampling period. For example, afirst camera associated with a desktop computer at work may capture afirst set of images of the end user and a second camera associated witha mobile phone of the end user may capture a second set of images of theend user. The individual mood classification corresponding with a firstmood sampling period may then be determined by applying facialexpression and mood detection techniques to the first set of images andthe second set of images. In some cases, the individual moodclassification may be determined using captured images, video, and/oraudio of the end user during the mood sampling period. In one example,captured audio of the end user may be used to detect particular wordsspoken by the end user.

In step 368, a group mood classification associated with a mood of agroup of people is determined. The group may include the end user or bea group affiliated with the end user. The group of people may comprise acompany as a whole, a division within a company, or a team of peopledesignated to work on a particular task. In one embodiment, the groupmood classification may correspond with a most frequent classificationof a plurality of individual mood classifications associated with thegroup of people. Individual mood classifications may comprise anumerical value associated with a mood classification spectrum. Forexample, at a low end of the mood classification spectrum may be angerand sadness, in the middle of the mood classification spectrum may beneutral, and at a high end of the mood classification spectrum may behappiness. In one embodiment, the mood classification spectrum maycorrespond with a numerical range from 1 to 100 with a 50 being assignedto a neutral mood classification. In the case that individual moodclassifications are assigned a numerical value, the group moodclassification may correspond with a weighted average of a plurality ofindividual mood classifications. In one example, the weights given tothe weighted average may be based on a member's seniority, rank, gradelevel, and/or position within an organization. One embodiment of aprocess for determining a group mood classification is described laterin reference to FIG. 6C.

In step 370, one or more rules corresponding with whether a suspiciousactivity has occurred are acquired. The one or more rules may be part ofa suspicious activity filter (or malicious activity filter). The one ormore rules may correspond with one or more weighting coefficients. Theone or more weighting coefficients may be used to weigh various factorsor rules when determining whether a suspicious activity filter has beensatisfied.

In some embodiments, the one or more rules may include detectingparticular facial expressions or gestures (facial gestures, bodygestures, and/or hand gestures) performed by the end user or detectingparticular phrases spoken by the end user expressing anger orfrustration. The one or more rules for determining when the end user isperforming suspicious activities may also take into account the time ofday and the location of the end user. The location of the end user maybe determined by acquiring GPS location information associated with amobile device used by the end user.

In step 372, the one or more weighting coefficients are adjusted basedon the suspicion level, the group mood classification, and theindividual mood classification. In one embodiment, periods of timeduring which a reduction in force occurs, employee layoffs occur, poorfinancial results are reported, or company stock decreases significantlyin value may correspond with a group mood classification that classifiesa mood of the group as anxious or sad. In some cases, the group moodclassification may be set by human resources personnel or automaticallydetermined via the aggregation and weighting of a plurality ofindividual mood classification. Periods of time that include dates thatare close to project deadlines may also cause the group moodclassification to be in an anxious or sad state. When the group moodclassification classifies a mood of the group as a whole to be anxiousor sad, then the one or more weighting coefficients may be increased inorder to perform a heightened degree of end user monitoring and totrigger mitigating actions to be performed for lower degrees ofsuspicious activity. In some cases, the one or more weightingcoefficients may be adjusted such that a suspicious activity filter maybe satisfied and trigger mitigating actions for lower degrees ofsuspicious activities if an employee has been tagged as a person ofinterest (e.g., tagged as a disgruntled employee) or if the group moodclassification corresponds with an anxious or sad state.

In step 374, the one or more rules and the one or more weightingcoefficients are outputted. In one embodiment, the one or more rules andthe one more weighting coefficients may be outputted as a part of asuspicious activity filter associated with an individual.

FIG. 4A is a flowchart describing one embodiment of a process forpreventing the transmission of false statements. In one embodiment, theprocess of FIG. 4A is performed by a mobile device, such as mobiledevice 140 in FIG. 1.

In step 402, an identification of an end user of a computing device isdetermined. The identification of the end user may be determined byapplying facial recognition techniques to one or more images captured bya front facing camera of the computing device. The one or more imagesmay be captured using a camera, such as front-facing camera 253 in FIG.2A. In one example, facial recognition techniques may determine theidentification of the end user based on a database of employee images.In some embodiments, video and/or audio associated with the end user maybe simultaneously captured while the end user operates the computingdevice.

In step 404, a malicious activity filter associated with theidentification of the end user is acquired. The malicious activityfilter may comprise one or more rules for determining when the end userhas performed a suspicious activity (e.g., an activity that requires aheightened degree of monitoring or precautions to be taken in order toprevent the improper transmission of information to others). The one ormore rules may include detecting particular facial expressions orgestures performed by the end user or detecting particular phrasesexpressing anger or frustration. The one or more rules for determiningwhen the end user is performing suspicious activities may also take intoaccount the time of day and the location of the end user. The locationof the computing device may be determined by acquiring GPS locationinformation associated with the computing device used by the end user.

In some embodiments, the malicious activity filter may be satisfied ifthe end user has displayed or expressed anger or frustration asdetermined by applying facial expression and mood detection techniquesto the images. In one example, if the end user is deemed to be in anangry, frightened, or anxious mood, then the malicious activity filtermay be satisfied. In another example, if the end user is deemed to beangry and they are on a watchlist for an organization (e.g., tagged as adisgruntled employee), then the malicious activity filter may besatisfied. In another embodiment, the malicious activity filter may besatisfied if the end user is deemed to be overly excited or overlyhappy. One embodiment of a process for acquiring a malicious activityfilter was described previously in reference to FIG. 3C.

In step 406, images of the end user are captured. The images may becaptured using a camera, such as front-facing camera 253 in FIG. 2A. Insome embodiments, video and/or audio associated with the end user may becaptured while the end user operates the computing device. The videoand/or audio acquired may be used to monitor the end user as the enduser operates the computing device (e.g., performs work-related taskssuch as drafting emails or editing spreadsheets). In some embodiments,the images of the end user may be captured using a plurality of cameraslocated around a work environment. In one example, a plurality ofcameras may be located within an office of the end user or in meetingrooms within the work environment.

In step 408, it is detected that a suspicious event has occurred basedon the malicious activity filter and the captured images. The suspiciousevent may be deemed to have occurred if the malicious activity filterhas been satisfied or when a combination of one or more rules fordetermining that a suspicious activity has occurred have been satisfied.In some cases, facial expressions and hand gestures performed by the enduser may be used to infer whether the end user is lying or performing amalicious activity. In one example, changes in facial expressions (e.g.,eyes narrowing, changes in blinking patterns, and changes in the shapeof the end user's eyebrows) or changes in facial characteristics (e.g.,eye dilation, changes in the size of the end user's iris, and thepresence of sweat on the end user's forehead) may be used to identifystress in the end user and to detect whether a suspicious event hasoccurred. The determination of whether a suspicious event has beendetected may also take into account the end user's facial expressionsand movements while the end user is performing a particular work-relatedtask, such as reading information downloaded from a secure network ordrafting an email message to be sent from the secure network. In somecases, a suspicious event may be detected when nervous or anxiousmovements performed by the end user are detected. In one example,excessive finger shaking beyond a baseline level of finger shakingtypically associated with the end user when the end user is controllinga touchscreen display or a virtual keyboard, such as virtual keyboard258 of FIG. 2B, may trigger the detection of a suspicious event. Inanother example, the performance of excessive nervous or anxiousgestures or movements such as excessive pacing, nail-biting, or hairpulling beyond a baseline level of activity may trigger the detection ofa suspicious event.

In step 410, a document (or other electronic file) that is being editedby the end user is identified in response to detecting the suspiciousevent. In one environment, the document may comprise an email message(or email) being drafted by the end user. The document may also comprisean email message, word processing document, spreadsheet, or presentationdocument that was amended by the end user within a recent time period(e.g., within the last five minutes). In one embodiment, the documentmay comprise the electronic file that is the highest active document inan application stack or workspace.

In step 412, at least a portion of the document is tagged with atruthfulness value based on the detection of the suspicious event. Thetruthfulness value may indicate whether a portion of the document (e.g.,a paragraph) is definitely true, definitely false, or is associated witha degree of truthfulness. In one embodiment, a portion of the documentmay be tagged with a degree of truthfulness that corresponds with adegree of deviation in meaning between the portion of the document beingtagged and a reference statement.

In some embodiments, the at least a portion of the document may betagged with a truthfulness value based on whether the end user hasperformed a suspicious event while drafting or editing the at least aportion of the document. In one example, if the end user performed aparticular facial expression while writing a particular portion of thedocument (e.g., while writing a particular sentence or paragraph), thenthe truthfulness value may be set based on the particular facialexpression. When the end user performs a facial expression thattypically indicates potential lying or nervousness while drafting aportion of the document, then the truthfulness value may be set toindicate uncertainty with regards to the truthfulness of statements madeby the end user.

In step 414, it is determined whether the at least a portion of thedocument includes a false statement. A document may be deemed to includea false statement if a first meaning corresponding with a statement madewithin the document (represented as a first semantic model) conflictswith a second meaning of a reference statement (represented as a secondsemantic model). In one embodiment, the document may be parsed forkeywords or phrases corresponding with sensitive information,confidential information, or personal information. Once the keywords orphrases have been parsed, then natural language processing techniques(e.g., natural language understanding techniques or machine readingcomprehension techniques) may be applied to identify a sentence (orclause) including a keyword and to identify one or more possiblesemantics corresponding with the sentence. After the natural languageprocessing techniques have been applied to the document of interest,then one or more reference documents may be analyzed in order to detectsemantic discrepancies between the document and the one or morereference documents. In some cases, the one or more reference documentsmay be deemed to include only true statements and any deviation ofmeaning found in the document may be deemed a false statement. In somecases, a degree of deviation may be determined and a false statement maybe detected only if the degree of deviation is above a threshold value.

In one embodiment, the one or more reference documents may compriseelectronic files stored on a secure network or stored on the computingdevice. The one or more reference documents may comprise web pages andemail messages that have been accessed by the end user. In anotherembodiment, an Internet search or intranet search may be performed toidentify one or more reference documents. One embodiment of a processfor determining whether a portion of a document includes a falsestatement is described later in reference to FIG. 4C.

In step 416, data transmission buffering is enabled. In one embodiment,the data transmission buffering may be enabled in response to detectingthe suspicious event in step 408. In another embodiment, thetransmission buffering may be enabled in response to determining thatthe document includes the false statement in step 414. The datatransmission buffering may buffer or delay the transmission of any datato any destination outside of the network. In some cases, a ten minutedelay may be used to allow for processing by human resources personnelto the contents of any data transmission in which a business valuerating or confidential information rating is above a particularthreshold.

In step 418, it is determined that the end user intends to transmit thedocument or a portion of the document containing the false statement. Inone embodiment, the determination of whether the end user intends totransmit the false statement may be performed in response to determiningthat the document includes the false statement. It may be determinedthat the end user intends to transmit the false statement when the enduser has initiated a data transfer. In one embodiment, it may bedetermined that the end user intends to transmit the false statement ifthe end user attempts to send an email message containing the falsestatement (e.g., the end user selects a send button associated withtransmission of the email message). In another embodiment, it may bedetermined that the end user intends to transmit the false statement ifthe end user attempts to initiate a document transfer (e.g., using FTP).

In some embodiments, it is determined that the end user intends totransmit the document (or a portion of the document) containing thefalse statement when the end user initiates a data transfer to a sharedlocation (e.g., saves a file or transmits a file to a location on anexternal file system or website that is accessible by multiple persons).In one example, the data transfer may correspond with a file transfer toa cloud storage service or online document sharing service. As the datatransfer may be performed after the end user has saved data to betransferred locally on their local computing device, in some cases, datafiles (e.g., word processing files, spreadsheet files, or image files)may be tagged as containing a false statement (or an associated degreeof deviation) prior the end user initiating the data transfer. In oneexample, a document may be tagged with a truthfulness value (or retaggedwith an updated truthfulness value) every time the document is saved(e.g., either explicitly saved by an end user or via an auto-saveutility).

In step 420, a mitigating action is performed in response to determiningthat the end user intends to transmit the false statement. In oneembodiment, the mitigating action may comprise an alert issued to humanresources personnel that requires authorization by the human resourcespersonnel before the document may be transmitted outside of a securenetwork. The mitigating action may include delaying the datatransmission for a period of time corresponding with a business valuerating of the document to be transmitted (e.g., delaying thetransmission of the document by an hour if the document includes a falsestatement regarding a key project or employee). In another embodiment,the mitigating action may comprise an alert issued to the end user ofthe computing device alerting them to the fact that their intended datatransmission includes a false statement. The end user may then berequired to confirm that they intend to make the data transmission.

In one embodiment, a watermark or a hidden source identifier may beattached to documents in the intended data transmission in order toprovide a trail in the event that the false statement is transmitted todestinations outside of a network. The hidden source identifier maycorrespond with an email address of the end user or an employee numberassociated with the end user.

In some embodiments, the document including a statement that has beendetermined to be a false statement may be annotated with informationassociated with one or more reference documents. In one example, theportion of the document including a false statement may be updated witha link to the one or more reference documents to which the falsestatement is attributed.

FIG. 4B is a flowchart describing an alternative embodiment of a processfor preventing the transmission of false statements. In one embodiment,the process of FIG. 4B is performed by a mobile device, such as mobiledevice 140 in FIG. 1.

In step 440, images of an end user of a computing device are captured.The images may be captured using a front-facing camera mounted on orembedded with the computing device. In step 442, audio associated withthe end user of the computing device is captured. The audio may becaptured using a microphone integrated with the computing device.

In step 444, baseline behavior associated with the end user isdetermined based on the images and the audio. The baseline behavior maybe derived over a first period of time (e.g., over a week of observationor a month of observation). The baseline behavior may comprise metricsincluding a median individual mood classification associated with theend user and/or the most frequent mood classification associated withthe end user during the first period of time. The baseline behaviorassociated with the end user may correspond with different times of theday and with different locations (e.g., a first baseline behavior may beassociated with an end user operating a computing device at work duringthe daytime and a second baseline behavior may be associated with theend user operating the computing device or a different computing deviceat home at night). Other baseline behaviors associated with the end usersuch as typical typing speeds, typical data downloads, and typicaldegrees of finger shaking may also be determined for different times ofthe day and for different locations. The location of the end user may bedetermined by acquiring GPS location information associated with acomputing device used by the end user.

In step 446, a deviation from the baseline behavior is detected based onthe images and the audio. In one embodiment, a deviation may be detectedif an individual mood classification of the end user is different from abaseline individual mood classification (e.g., a median moodclassification or the individual mood classification with the highestfrequency during the first period of time) associated with the end user.In some cases, on a weekly basis, an individual mood classification maybe determined for the end user. The individual mood classification maydepend on a frequency of particular facial expressions performed by theend user during a sampling period subsequent to the first period of time(i.e., after the time period associated with the baseline moodclassification). On a yearly basis, a baseline individual moodclassification may correspond with the median individual moodclassification or the most frequent mood classification associated withthe end user over the course of the year. When the individual moodclassification for a particular week is different from the baselineindividual mood classification, then a deviation from baseline behaviormay be detected. In other cases, a baseline individual moodclassification may be computed every week and individual moodclassifications may be computed every hour.

In step 448, a document that is being edited by the end user isidentified in response to detecting the deviation from the baselinebehavior. The document may comprise a draft email message, a wordprocessing document or other electronic file. In step 450, it isdetermined whether at least a portion of the document includes a falsestatement. In one embodiment, the document may be deemed to include afalse statement if a first meaning corresponding with a statement madewithin the document (represented as a first semantic model) conflictswith a second meaning of a reference statement (represented as a secondsemantic model). One embodiment of a process for determining whether aportion of a document includes a false statement is described later inreference to FIG. 4C.

In step 452, an alert is issued to the end user regarding the falsestatement. The end user may also be provided with one or more links tosource information disagreeing with the false statement (e.g., a link toa reference document associated with a true statement).

FIG. 4C is a flowchart describing one embodiment of a process fordetermining whether a document includes a false statement. The processdescribed in FIG. 4C is one example of a process for implementing step414 in FIG. 4A or for implementing step 450 in FIG. 4B. In oneembodiment, the process of FIG. 4C is performed by a mobile device, suchas mobile device 140 in FIG. 1.

In step 462, a document is acquired. The document may correspond with anemail or word processing document that is open on a computing deviceused by the end user or is being actively edited by the end user usingthe computing device. In step 464, one or more keywords are identifiedwithin the document. The one or more keywords may correspond withconfidential information or key business or organizational terms thatare predefined by human resources personnel. The one or more keywordsmay be identified within the document using natural language processingtechniques (e.g., language parsing). In step 466, one or more phrasesassociated with each of the one or more keywords are identified. The oneor more phrases may be identified using natural language processingtechniques. In one example, a sentence including a particular keywordmay be identified as a phrase.

In step 468, one or more search locations are determined. The one ormore search locations may correspond with locations on a network orcomputing system in which to find reference documents for comparing theone or more phrases. The one or more search locations may correspondwith locations on the Internet (e.g., a set of publicly accessiblewebpages) or a company intranet. The one or more search locations maycorrespond with file server locations on a secure network or particularfiles on a data storage system. In one embodiment, the one or moresearch locations may correspond with a file server and an intranet foran organization to which an end user of a computing device isaffiliated. The one or more search locations may also comprise one ormore computing devices used by the end user (i.e., local hard drives).

In some cases, the one or more search locations may be filtered bysearching an end user's webpage viewing history, network viewinghistory, history of file server accesses, history of accesses todocuments located on a secure network, or a list of previously accesseddocuments by the end user. The end user's emails (drafted, sent, andreceived) may comprise one of the locations to be searched for referenceinformation.

In step 470, the one or more networks are searched for sourceinformation corresponding with the one or more phrases. The sourceinformation may comprise one or more reference documents that may bedeemed to include only true statements and any deviation of meaningfound in the document acquired in step 462 may be deemed a falsestatement.

In step 472, the source information is acquired. In one example, thesource information may be acquired from a secure file server. In anotherexample, the source information may be acquired from the Internet. Instep 474, it is determined whether the source information agrees with(or is semantically consistent with) the one or more phrases. Thedetermination of whether the source information agrees with the one morephrases may comprise applying natural language processing techniques tothe source information and the one or more phrases. In one example, thenatural language processing techniques may first be applied to the oneor more phrases (from the document), then the source information (e.g.,a reference document) may be analyzed in order to detect semanticdiscrepancies between the one or more phrases and the sourceinformation. In step 476, a false statement indicator associated withwhether the source information agrees with or is consistent with the oneor more phrases is outputted. The source information or a link to thesource information may also be outputted.

FIG. 5A is a flowchart describing one embodiment of a process forpreventing the transmission of private information. In one embodiment,the process of FIG. 5A is performed by a mobile device, such as mobiledevice 140 in FIG. 1.

In step 502, a request for a data transmission is detected. In oneembodiment, the request may comprise a request to transmit a documentover a network. In one example, the request may comprise a request totransmit an email message (e.g., an end user may hit a send buttonassociated with transmission of the email message). In another example,the request may comprise a request to initiate a document transfer(e.g., using FTP) to computers located outside of a network.

In step 504, one or more documents associated with the request aredetermined. The one or more documents may include an email message orword processing document. In step 506, an identification of a personwithin the one or more documents is detected. The identification of theperson may include a name associated with the person or anidentification number associated with the person (e.g., a SocialSecurity number).

In step 508, it is determined whether the one or more documents includeprivate information associated with the person. In one embodiment,private information may comprise information associated with the personthat is not publicly available. In one example, the private informationmay include the person's Social Security number, private home address,private phone number, medical records, and/or financial records. Theprivate information may be identified within the one or more documentsusing keyword matching, string matching, or natural language processingtechniques.

In step 510, it is determined whether to request authorization from theperson prior to performing the data transmission. In one embodiment,private information may be classified as always requiring authorizationbefore transmission, never requiring authorization before transmission,or authorization may be inferred based on the person's authorizationhistory.

In step 512, images of the person are captured while displaying anauthorization request associated with the data transmission. The imagesmay be captured using a front-facing camera associated with a computingdevice used to display the authorization request to the end user. Instep 514, a reaction is determined based on the captured images. Thereaction may be determined based on facial expressions and/or gesturesperformed by the end user while the end user reads the authorizationrequest. The reaction may be deemed to correspond with an inferredapproval if the person accepts the authorization request and displaysfacial expressions that correspond with a neutral or happy emotionalstate. The reaction may be deemed to correspond with an inferreddisapproval if the person rejects the authorization or if the personaccepts the authorization request but displays facial expressions thatcorrespond with an angry or frustrated emotional state.

In step 516, the reaction is mapped to a response to the authorizationrequest by the person and stored in a user profile associated with theperson. For example, the user profile may include a mapping of an enduser's reaction to an authorization request associated with theirmedical records. The mapping may be stored on a per requestor basis(e.g., the mapping may be unique to a third party requesting the privateinformation). The mapping may also correspond with a particular groupidentifier (e.g., persons associated with a health care organization orcompany). For example, an end user's reaction to an authorizationrequest for their medical records by someone identified as belonging toa health care organization may be different from an authorizationrequest for their medical records by a member of the public.

In one example, the private information associated with a person maycomprise medical records and a request for documents including theprivate information may be made by a medical researcher. In this case, amapping of the person's reaction to an authorization request for accessto their medical records by the medical researcher may be stored in theperson's user profile. The person's user profile may be stored on aserver associated with the person's health care provider.

In step 518, authorization from the person to perform the datatransmission is inferred based on the mapping. One embodiment of aprocess for inferring authorization is described later in reference toFIG. 5C. In step 520, the data transmission is performed includingtransmission of the one or more documents in response to inferringauthorization from the person. In one example, the private informationassociated with a person may comprise their private home address and arequest for a webpage including the private information may be made bythe third party to a social networking website. The social networkingwebsite may store a user profile associated with the person and inferauthorization to provide access to the webpage including the person'sprivate home address based on mappings associated with the person'sprivate home address stored in the user profile. In one embodiment, adynamically constructed webpage may be generated that includes portionsof a document including private information up to a level ofauthorization (i.e., the rest of the document associated with higherauthorization levels may be hidden and not released). In one example, adynamically constructed webpage may provide access to a person's privatehome address, but not their Social Security number.

In some embodiments, the person's reaction to an authorization requestmay be classified according to the identity, group identifier (e.g.,persons associated with a particular organization or company), role(e.g., an employment classification such as manager), certifications,credentials, referrals (e.g., from trusted sources who can vouch for therequestor), location, and time of day of the requesting party. Theclassification of the requesting party may be used to automatesubsequent responses from other requesting parties with similarcharacteristics to those parties for which a reaction has already beencaptured and stored in a user profile (e.g., it may be inferred that aperson's reaction to an authorization request for different peopleassociated with the same role or credentials may be the same). In oneexample, a user profile may include a mapping associated with aparticular third party (e.g., a first person affiliated with a healthcare organization) and a requesting third party (e.g., a second personaffiliated with the health care organization) may comprise a third partydifferent from the particular third party

In some embodiments, a business value rating, confidential informationrating, semantic similarity, or graphic similarity associated with oneor more documents to be released may be used to infer authorization forthe one or more documents (e.g., it may be inferred that a person'sreaction to authorization requests for similar documents may be thesame). In one example, a requesting third party may be associated with ahealth care organization and a first set of medical records requestedmay be semantically similar and/or graphically similar to a second setof medical records for which authorization has already been granted tothe requesting third party. In this case, authorization to the first setof medical records may be given to the requesting third party based on adegree of semantic similarity and/or graphical similarity between thefirst set of medical records and the second set of medical records.

FIG. 5B is a flowchart describing one embodiment of a process forpreventing the transmission of private information. In one embodiment,the process of FIG. 5B is performed by a mobile device, such as mobiledevice 140 in FIG. 1.

In step 542, it is detected that a third party has requested access toprivate information associated with a person. The third party mayrequest access to a document including the private information stored ona website. In one example, a web server associated with a website maydetect that the third party is requesting access to the privateinformation stored on the website. In step 543, a user profileassociated with the person is acquired. In step 544, it is determinedwhether to request authorization from the person prior to grantingaccess to the private information based on the user profile. In oneembodiment, authorization to the private information may be required ifa classification for the private information explicitly requiresauthorization or if a user profile associated with the person does notinclude a mapping for the private information to the third party.

In step 546, images of the person are captured while displaying anauthorization request for the private information associated with thethird party. The images may be captured using a front-facing cameraassociated with a computing device used to display the authorizationrequest to the end user. In step 548, a reaction is determined based onthe captured images. The reaction may be determined based on facialexpressions and/or gestures performed by the end user while the end userreads the authorization request. The reaction may be deemed tocorrespond with an inferred approval if the person accepts theauthorization request and displays facial expressions that correspondwith a neutral or happy emotional state. The reaction may be deemed tocorrespond with an inferred disapproval if the person rejects theauthorization or if the person accepts the authorization request butdisplays facial expressions that correspond with an angry or frustratedemotional state.

In step 550, the reaction is mapped to a response to the authorizationrequest by the person and stored in the user profile associated with theperson. For example, the user profile may include a mapping of an enduser's reaction to an authorization request associated with theirfinancial records. The mapping may be stored on a per requestor basis(e.g., the mapping may be specific to the third party requesting theprivate information).

In step 552, authorization from the person to grant access to theprivate information is inferred based on the mapping. One embodiment ofa process for inferring authorization is described later in reference toFIG. 5C. In step 554, the private information is transmitted to thethird party in response to inferring authorization from the person. Inone example, the private information associated with a person maycomprise their private phone number and a request for a webpageincluding the private information may be made by the third party to awebsite. A server of the website may store a user profile associatedwith the person and infer authorization to provide access to the webpageincluding the person's private phone number based on mappings associatedwith the person's private phone number stored in the user profile.

FIG. 5C is a flowchart describing one embodiment of a process forinferring authorization to private information. The process described inFIG. 5C is one example of a process for implementing step 518 in FIG. 5Aor for implementing step 552 in FIG. 5B. In one embodiment, the processof FIG. 5C is performed by a mobile device, such as mobile device 140 inFIG. 1.

In step 562, a user profile associated with a person is acquired. Theuser profile may include one or more mappings of user reactions to anauthorization request for private information associated with theperson. In step 564, a third party associated with a request for theprivate information is identified. The third party may be identified bya name of the third party or an email address associated with the thirdparty. The third party may also be identified as belonging to aparticular group or organization (e.g., employed by a particularcompany).

In step 566, a classification for the private information requested isdetermined. The private information may comprise information associatedwith the person that is not publicly available such as the person'sSocial Security number, private home address, or medical records. In oneembodiment, private information may be classified as always requiringauthorization before transmission, never requiring authorization beforetransmission, or authorization may be inferred based on the person'sauthorization history.

In step 568, a user reaction to the request for private information isdetermined (or inferred) based on the identification of the third party,the classification of the private information, and the one or moremappings. In one example, a first mapping of the one or more mappingsmay correspond with a positive reaction to requests to the person'sprivate home address by a third party identified by a particular emailaddress.

In step 570, a request for authorization is outputted to the person ifthe user reaction determined in step 568 comprises a negative reaction.The negative reaction may correspond with facial expressions and/orgestures performed by the person associated with an angry or frustratedemotional state. In this case, access to the private information mayonly be provided to the third party if the person explicitly authorizesaccess to the private information. In step 572, authorization for accessto the private information by the third party is outputted if the userreaction determined in step 568 comprises a positive reaction. Thepositive reaction may correspond with facial expressions and/or gesturesperformed by the person associated with a neutral or happy emotionalstate.

FIG. 6A is a flowchart describing one embodiment of a process forpreventing the transmission of sensitive information. In one embodiment,the process of FIG. 6A is performed by a mobile device, such as mobiledevice 140 in FIG. 1. The process of FIG. 6A may also be performed by aserver, such as server 160 in FIG. 1.

In step 602, it is detected that an end user of a computing device isediting a document (or other electronic file) using the computingdevice. The document may be stored locally on the computing device orremotely on a remote file server. The document may detected as edited bythe end user if a document state of the document corresponds with amodified state and the end user has provided input to the computingdevice to change the document state. The document may comprise an emailmessage, word processing document, spreadsheet, or other electronicfile. The end user may edit the document by modifying the document,adding text or symbols to the document, or deleting text or symbols fromthe document.

In step 604, it is detected that the end user intends to transmit thedocument to a second person. It may be detected that the end userintends to transmit the document to the second person if an emailaddress associated with the second person is one of the target emailaddresses used by the end user to transmit the document to an intendedrecipient. In one embodiment, it may be determined that the end userintends to transmit the document when the end user has initiated a datatransfer including the document. In one embodiment, it may be determinedthat the end user intends to transmit the document if the end userattempts to send an email message including the document (e.g., the enduser selects an email send button within an email application). Inanother embodiment, it may be determined that the end user intends totransmit the document if the end user attempts to initiate an electronicdocument transfer (e.g., using FTP).

The second person may be associated with a target email address (i.e.,an email address of an intended recipient of the document). In oneembodiment, it may be detected that the end user intends to transmit thedocument to a second person if the target email address associated withthe second person is different from one or more email addressesassociated with the end user. In one example, the end user may beassociated with a work email address and a personal email address. Ifthe target email address is different from the work email address andthe personal email address, then it may be determined that the end userintends to transmit the document to a different person. In some cases,the determination of whether the end user accidentally initiated adocument transfer or expresses an unintended transmission of thedocument after initiating the document transfer may only be performed ifthe target email address is different from one or more email addressesassociated with the end user (i.e., documents that are transmitted toyourself won't be analyzed).

In step 606, images of the end user are captured in response todetecting that the end user intends to transmit the document to thesecond person. The images may be captured using a camera, such asfront-facing camera 253 in FIG. 2A. In some embodiments, video and/oraudio associated with the end user may be captured upon detection thatthe end user intends to transmit the document to another person.

In step 607, a data transmission delay is determined. In one embodiment,the data transmission delay may be set using contextual information suchas a time of day, a day of the week, a mood of an organization of whichthe end user is a member, and/or a mood of the end user. The mood of theend user may correspond with an individual mood classificationassociated with the end user. In some cases, the data transmission delaymay comprise a baseline delay value (e.g., one minute) that may beadjusted based on the contextual information (e.g., if the mood of theend user is angry or frustrated, then an additional five minute delaymay be added to the baseline delay value).

In step 608, a transmission of the document is delayed by the datatransmission delay in response to detecting that the end user intends totransmit the document. In one embodiment, the document may be placedinto a buffer (e.g., located on a mail server) and withheld fromtransmission until the data transmission delay has passed.

In step 610, it is determined whether the end user has had a negativereaction within a first period of time after detecting that the end userintends to transmit the document based on the images. The first periodof time may correspond with the data transmission delay. For example, ifthe data transmission delay comprises a six minute delay, then the firstperiod of time may be set to the six minute delay. In one embodiment,the negative reaction may correspond with facial expressions and/orgestures performed by the end user associated with an angry orfrustrated emotional state. The negative reaction may be determined byapplying facial expression and mood detection techniques to the capturedimages.

In another embodiment, the negative reaction may correspond with facialexpressions and/or gestures performed by the end user associated with asurprised emotional state. For example, if the end user performs facialexpressions such as holding an open mouth with raised eye brows for morethan a threshold period of time (e.g., five seconds) or performsgestures such as covering their mouth with their hands for more than thethreshold period of time, then the end user may be deemed to be in asurprised or panicked emotional state. In some embodiments, audio may becaptured along with the images and analyzed for particular words such as“oh no” or “darn it” in order to detect a surprised or panickedemotional state.

In step 612, a confirmation to perform the document transmission isrequested in response to the negative reaction. In one embodiment, aconfirmation request may be sent to the end user and confirmation fromthe end user may be required before performing the document transmission(or other data transmission including the document). In some cases, theend user may confirm the intended transmission of the document byperforming a particular gesture (e.g., a thumbs up) or speaking aparticular phrase (e.g., “I confirm”). In step 614, the document istransmitted in response to receiving the confirmation. The document maybe transmitted to an email address associated with the second person. Inthe event that the end user does not provide the confirmation necessaryto transmit the document, the document transmission may be canceled orterminated.

FIG. 6B is a flowchart describing one embodiment of a process forpreventing the transmission of sensitive information. In one embodiment,the process of FIG. 6B is performed by a mobile device, such as mobiledevice 140 in FIG. 1. The process of FIG. 6B may also be performed by aserver, such as server 160 in FIG. 1.

In step 632, it is detected that an end user of a computing device isediting a document. The document may be stored locally on the computingdevice or remotely on a remote file server (e.g., the document may becontrolled by the end user using the computing device even though thedocument is stored on a remote server). The document may be detected asbeing edited by the end user if a document state of the documentcorresponds with a modified state and the end user has provided input tothe computing device to change the document state. The document maycomprise an email message, word processing document, spreadsheet, orother electronic file. The end user may edit the document by modifyingthe document, adding text or symbols to the document, or deleting textor symbols from the document.

In step 634, a time of day and a location associated with the end userare determined. The location of the end user may correspond with a GPSlocation of the computing device. In step 636, a group moodclassification associated with a mood of a group of people isdetermined. The group of people may include the end user. One embodimentof a process for determining a group mood classification is describedlater in reference to FIG. 6C.

In step 638, an individual mood classification associated with a mood ofthe end user is determined. The mood of the end user may be determinedover a period of time (e.g., within a four hour period or over a 24-hourperiod). The individual mood classification may be determined byapplying facial expression and mood detection techniques to capturedimages of the end user over the period of time. The individual moodclassification may classify a mood of the end user as angry, frustrated,sad, anxious, neutral, or happy. One embodiment of a process fordetermining an individual mood classification was described earlier inreference to FIG. 3C.

In some cases, the individual mood classification may be determinedbased on baseline mood characteristics associated with typical end userfacial expressions that occur during different times of the day and/orwhen the end user is working in different locations (e.g., the number oftimes that the end user typically makes a sad face or displays anger maybe lower at night at home than during the day at work). Other baselinebehaviors associated with the end user such as typical degrees of handshaking during different times of the day or when in different locationsmay be taken into account when determining the individual moodclassification.

In step 640, a buffer delay associated with a document transmission isassigned based on the time of day, the location of the end user, thegroup mood classification, and the individual mood classification. Insome cases, the buffer delay may be increased when the end user is awayfrom a work environment (e.g., at home) or when the end user is editingthe document during a time that the end user is not typically working(e.g., deviates from baseline working hours). In step 642, it isdetected that the end user intends to transmit the document to a secondperson. It may be detected that the end user intends to transmit thedocument to the second person if an email address associated with thesecond person is one of the target email addresses used by the end userto transmit the document to an intended recipient. In one embodiment, itmay be determined that the end user intends to transmit the documentwhen the end user has initiated a data transfer including the document.In one embodiment, it may be determined that the end user intends totransmit the document if the end user attempts to send an email messageincluding the document (i.e., the end user hits a send button within anemail application).

In step 644, images of the end user are captured. The images of the enduser may be captured in response to detecting that the end user intendsto transmit the document to the second person. The images may becaptured using a camera, such as front-facing camera 253 in FIG. 2A. Insome embodiments, video and/or audio associated with the end user may becaptured upon detection that the end user intends to transmit thedocument to another person.

In step 646, transmission of the document is delayed by the bufferdelay. In some cases, the buffer delay may be adjusted based oncontextual information (e.g., if the mood of the end user is angry orfrustrated, then an additional five minute delay may be added to abaseline buffer delay value).

In step 648, it is determined whether the end user has had a negativereaction within a first period of time associated with the buffer delaybased on the images. In one embodiment, the negative reaction maycorrespond with facial expressions and/or gestures performed by the enduser associated with an angry or frustrated emotional state. Thenegative reaction may be determined by applying facial expression andmood detection techniques to the captured images. In another embodiment,the negative reaction may correspond with facial expressions and/orgestures performed by the end user associated with a surprised emotionalstate. For example, if the end user may performs facial expressions suchas holding an open mouth with raised eye brows for more than a thresholdperiod of time (e.g., five seconds) or gestures such as covering theirmouth with their hands for more than the threshold period of time, thenthe end user may be deemed to be in a surprised or panicked emotionalstate. In some embodiments, audio may be captured along with the imagesand analyzed for particular words such as “oh no” or “darn it” in orderto detect a surprised or panicked emotional state. In step 650, thetransmission of the document is canceled in response to the negativereaction of the end user.

FIG. 6C is a flowchart describing one embodiment of a process fordetermining a group mood classification. The process described in FIG.6C is one example of a process for implementing step 636 in FIG. 6B. Inone embodiment, the process of FIG. 6C is performed by a mobile device,such as mobile device 140 in FIG. 1.

In step 672, a plurality of identifications associated with a pluralityof people is determined. The plurality of people may be associated withan organization, a company, or a team of people working on a commonproject. The plurality of identifications may comprise names oridentification numbers (e.g., employee identification numbers)associated with the plurality of people. In step 674, a plurality ofindividual mood classifications associated with the plurality ofidentifications is determined. In one embodiment, the plurality ofindividual mood classifications may be acquired from an aggregationserver that aggregates individual mood classifications computed byvarious computing devices used by the plurality of people. Oneembodiment of a process for determining an individual moodclassification was described earlier in reference to FIG. 3C.

In step 676, a weighting of the plurality of individual moodclassifications is determined. The weighting may depend on contextualorganizational information such as whether stressful events haveoccurred to an organization such as a recent reduction in force, buyoutrumors, recent employee layoffs, recent reporting of poor financialresults, or recent changes in the value of company stock. In step 678, agroup mood classification is determined based on the plurality ofindividual mood classifications and the weighting determined in step676. In one embodiment, the group mood classification associated with afirst time period may correspond with the most frequent individual moodclassification of the plurality of individual mood classifications overthe first time period. For example, if each of the plurality ofindividual mood classifications is assigned to one of ten different moodclassifications, then the group mood classification may be assigned tothe most frequent classification of the ten different moodclassifications. In step 680, the group mood classification isoutputted.

FIG. 7A is a flowchart describing one embodiment of a process fordetermining a mood of an organization and for detecting shifts in themood of the organization. In one embodiment, the process of FIG. 7A isperformed by a server, such as server 160 in FIG. 1.

In step 702, an electronic message is transmitted to a plurality oftarget addresses associated with the group of people. The group ofpeople may be associated with an organization, a company, or a team ofpeople working on a common project. The target addresses may comprise aplurality of target email addresses. The electronic message may includea message from a human resources department (e.g., informing employeesof a change in benefits) or a message from an executive of a company(e.g., discussing news regarding the company). The electronic messagemay be transmitted simultaneously to each of the plurality of targetaddresses. The electronic message may comprise an email message.

In step 704, it is detected that a first person associated with a firsttarget address of the plurality of target addresses caused theelectronic message to be displayed. In one embodiment, the first personmay cause the electronic message to be displayed by selecting theelectronic message within an email application. As the electronicmessage may be transmitted to many different people, each person mayopen or read the electronic message at different times (i.e., thereading of the electronic message may be an asynchronous event).

In step 706, images of the first person are captured in response todetecting that the first person has caused the electronic message to bedisplayed. The images may be captured using a front-facing cameraassociated with a computing device displaying the electronic message.The images may be captured in response to detecting that the firstperson is reading the electronic message. In one embodiment, eyetracking techniques may be used to determine if the end user is readingthe electronic message. For example, the end user may be deemed to bereading the electronic message if they are looking at a displaydisplaying the electronic message and their eye movements correspondwith a tracking of words in the electronic message.

In step 708, an initial reaction is determined based on the capturedimages. The initial reaction may be determined by applying facialexpression and mood detection techniques to the captured images. In oneexample, the initial reaction may correspond with a surprised reactionor an angry reaction.

In step 710, an individual mood classification is determined based onthe captured images. Facial expression and mood detection techniques maybe used to determine a mood classification for an individual or a groupof individuals. The facial expression and mood detection techniques mayidentify facial descriptors and facial landmarks from the capturedimages. The facial descriptor may comprise information regarding selectfacial features of the first person (e.g., the relative position of theperson's eyes, nose, cheekbones, and/or jaw). The select facial featuresmay be extracted or detected within the captured images by applyingvarious image processing techniques such as object recognition, featuredetection, corner detection, blob detection, and edge detection methodsto the captured images.

The individual mood classification of the end user may be determinedover a period of time (e.g., within a four hour period or over a 24-hourperiod). The individual mood classification may be determined byapplying facial expression and mood detection techniques to capturedimages of the end user over the period of time. The individual moodclassification may classify a mood of the end user as angry, frustrated,sad, anxious, neutral, or happy using a numerical value.

In some cases, the individual mood classification may be determinedbased on baseline mood characteristics associated with typical end userfacial expressions that occur during different times of the day and/orwhen the end user is working in different locations (e.g., the number oftimes that the end user typically makes a sad face or displays anger maybe lower at night at home than during the day at work). Other baselinebehaviors associated with the end user such as typical degrees of handshaking during different times of the day or when in different locationsmay be taken into account when determining the individual moodclassification. The location of the end user may be determined byacquiring GPS location information associated with a mobile device usedby the end user (e.g., the end user's cell phone). In some cases, otherbaseline indicators of mood, such as a pulse rate or respiration rateassociated with the end user, may also be used for determining anindividual mood classification.

In step 712, a group reaction is determined based on a plurality ofinitial reactions including the initial reaction of the first person. Inone embodiment, the group reaction may correspond with the most frequentreaction of the plurality of reactions. In some cases, theclassifications used for individual moods may also be used forindividual reactions. An individual reaction classification may bedetermined using a shorter period of time than an individual moodclassification. For example, an individual reaction classification maycorrespond with a first period of time (e.g., 30 seconds) and anindividual mood classification may correspond with a second period oftime (e.g., 24 hours).

In step 714, a group mood classification is determined based on aplurality of individual mood classifications including the individualmood classification associated with the first person. In one embodiment,the group mood classification may correspond with a most frequentclassification of a plurality of individual mood classificationsassociated with the group of people. One embodiment of a process fordetermining a group mood classification was described previously inreference to FIG. 6C.

In step 716, it is determined whether the group mood classification hasdeviated from a baseline group mood classification by a threshold amount(or a threshold value). In one example, a mood classification spectrum(or order of classifications) may be created. For example, at a low endof the classification spectrum may be anger and sadness, in the middleof the classification spectrum may be neutral, and at a high end of theclassification spectrum may be happiness. A numerical range may beassigned to the mood classification spectrum and if a deviation from aparticular numerical value is identified, then a deviation may betriggered.

In one embodiment, the mood classification spectrum may correspond witha numerical range from 1 to 100 with a 50 being assigned to a neutralmood classification and 100 being assigned to the happiest emotionalstate. Assuming a threshold value of 15, if a baseline group moodclassification is set to a value of 70 and the group mood classificationis determined to be 50, then a deviation may be detected as thethreshold value has been exceeded. The deviation may be a positivedeviation (i.e., a shift towards happiness) or a negative deviation(i.e., a shift towards anger).

In step 718, the group reaction and the group mood classification isoutputted if the group mood classification has deviated from thebaseline group mood classification by more than a threshold value. Insome cases, the group reaction may be transmitted (e.g., as part of anemail alert) to human resources personnel or a manager of anorganization if the group mood classification has deviated from thebaseline group mood classification by more than a threshold value andthe deviation is a negative deviation.

FIG. 7B is a flowchart describing one embodiment of a process fordetecting a group response to an electronic message. In one embodiment,the process of FIG. 7B is performed by a server, such as server 160 inFIG. 1.

In step 722, a baseline group mood classification associated with a moodof a group of people during a first time period is determined. In somecases, the first time period may comprise a six-month period or athree-month period. In step 724, an electronic message is transmitted toa plurality of target addresses associated with the group of people. Theelectronic message may comprise an email message. The electronic messagemay include a message from a human resources department (e.g., informingemployees of a change in benefits) or a message from an executive of acompany (e.g., a resignation letter that has not been vetted by humanresources personnel).

In step 726, it is detected that a first set of the group of people haveread the electronic message. The first set may comprise the first 10people of the group of people to read the electronic message. In somecases, the first set of the group of people may comprise a predefinedgroup of people (e.g., defined by human resources personnel) who aretransmitted electronic messages before they are broadcast to the rest ofthe group of people.

In step 728, a second group mood classification associated with thefirst set of the group of people is determined subsequent to detectingthat the first set of the group of people have read the electronicmessage. In one embodiment, eye tracking techniques may be used todetermine if the first set of the group of people have read theelectronic message. For example, the each person of the first set may bedeemed to have read the electronic message if they looked at a displaydisplaying the electronic message and their eye movements correspondwith a tracking of words in the electronic message. In some embodiments,the second group mood classification may correspond with a most frequentclassification of a plurality of individual mood classificationsassociated with the first set. One embodiment of a process fordetermining a group mood classification was described previously inreference to FIG. 6C.

In step 730, it is determined whether the second group moodclassification has deviated from the baseline group mood classificationby more than a threshold amount. In one embodiment, a moodclassification spectrum may be created along a numerical range (e.g.,from 1 to 100). In one example, at a low end of the classificationspectrum may be anger and sadness, in the middle of the classificationspectrum may be neutral, and at a high end of the classificationspectrum may be happiness. A deviation from the baseline group moodclassification may be identified if the second group mood classificationdeviates from the baseline group mood classification by more than athreshold value associated with the threshold amount (e.g., more than15%). For example, given a threshold value of 15, if a baseline groupmood classification is set to a value of 50 and the second group moodclassification is determined to be 30, then a deviation may be detectedas the threshold value has been exceeded. The deviation may be apositive deviation (i.e., a shift towards happiness) or a negativedeviation (i.e., a shift towards anger).

In step 731, an alert is outputted if the second group moodclassification has deviated from the baseline group mood classification.In step 732, the second group mood classification is outputted if thesecond group mood classification has deviated from the baseline groupmood classification. In some cases, the alert may be transmitted tohuman resources personnel or a manager of an organization if thedeviation is a negative deviation. In some cases, the alert may comprisean email message, instant message, tweet, or other electronicnotification. The electronic notification (or electronic message) may besent to an automated response system or an email address (e.g., an emailaddress associated with human resources personnel).

FIG. 7C is a flowchart describing one embodiment of a process fortransmitting an electronic message based on reactions of a group ofpeople. In one embodiment, the process of FIG. 7C is performed by aserver, such as server 160 in FIG. 1. The server may comprise an emailserver.

In step 742, a plurality of target addresses associated with a group ofpeople is determined. The plurality of target addresses may correspondwith a plurality of email addresses associated with a group of people ina company or other organization. In step 744, a first set of addressesof the plurality of target addresses is determined. The first set ofaddresses may correspond with a predefined group of people (e.g.,defined by human resources personnel) who are transmitted electronicmessages before they are broadcast to other people.

In step 746, electronic message is transmitted to the first set ofaddresses. The electronic message may be transmitted to the first set ofaddresses via email or text messaging. In one embodiment, the electronicmessage may comprise an email message. In step 748, it is detected thata first person associated with a first address of the first set ofaddresses has caused electronic message to be displayed. In oneembodiment, the first person may cause the electronic message to bedisplayed by selecting the electronic message within an emailapplication. In another embodiment, the first person may cause theelectronic message to be displayed by selecting the electronic messagewithin an electronic message viewing application (e.g., as a feature ofa social networking website).

In step 750, images of the first person are captured in response todetecting that the first person has caused electronic message to bedisplayed. The images may be captured using a front-facing cameraassociated with a computing device displaying the electronic message.The images may be captured in response to detecting that the firstperson is reading (or has started reading) the electronic message. Inone embodiment, eye tracking techniques may be used to determine if thefirst person is reading or has started reading the electronic message.For example, the first person may be deemed to be reading the electronicmessage if they are looking at a display displaying the electronicmessage and their eye movements correspond with a tracking of words inthe electronic message.

In step 752, an initial reaction is determined based on the capturedimages. The initial reaction may be determined by applying facialexpression and mood detection techniques to the captured images. In oneexample, the initial reaction may correspond with a surprised reactionor an angry reaction. In step 754, a first group reaction is determinedbased on a plurality of initial reactions including the initial reactionof the first person. The plurality of initial reactions may correspondwith a plurality of different people associated with the first set ofaddresses.

In step 756, it is determined whether to transmit the electronic messageto a second set of addresses of the plurality of target addresses basedon the first group reaction. The electronic message may be transmittedto the second set of addresses if the first group reaction comprises apositive reaction or a non-negative reaction. The first set of addressesmay comprise a first subset of the plurality of target addresses and thesecond set of addresses may comprise the remainder of the plurality oftarget addresses. The second set of addresses may be different from thefirst set of addresses (i.e., correspond with two different groups ofpeople). In one embodiment, if the first group reaction comprises anegative reaction, then the electronic message may not be transmitted tothe second set of addresses and an alert may be issued to humanresources personnel that the first group reaction comprised a negativereaction. In step 758, the electronic message is transmitted to thesecond set of addresses subsequent to determining the first groupreaction.

The disclosed technology may be used with various computing systems.FIG. 8 depicts one embodiment of a mobile device 8300, which includesone example of a mobile implementation for mobile device 140 in FIG. 1.Mobile devices may include laptop computers, pocket computers, mobilephones, personal digital assistants, tablet computers, and handheldmedia devices that have been integrated with wirelessreceiver/transmitter technology.

Mobile device 8300 includes one or more processors 8312 and memory 8310.Memory 8310 includes applications 8330 and non-volatile storage 8340.Memory 8310 can be any variety of memory storage media types, includingnon-volatile and volatile memory. A mobile device operating systemhandles the different operations of the mobile device 8300 and maycontain user interfaces for operations, such as placing and receivingphone calls, text messaging, checking voicemail, and the like. Theapplications 8330 can be any assortment of programs, such as a cameraapplication for photos and/or videos, an address book, a calendarapplication, a media player, an internet browser, games, an alarmapplication, and other applications. The non-volatile storage component8340 in memory 8310 may contain data such as music, photos, contactdata, scheduling data, and other files.

The one or more processors 8312 also communicates with dedicated audioserver 8309, with RF transmitter/receiver 8306 which in turn is coupledto an antenna 8302, with infrared transmitter/receiver 8308, with globalpositioning service (GPS) receiver 8365, and with movement/orientationsensor 8314 which may include an accelerometer and/or magnetometer. RFtransmitter/receiver 8308 may enable wireless communication via variouswireless technology standards such as Bluetooth® or the IEEE 802.11standards. Accelerometers have been incorporated into mobile devices toenable applications such as intelligent user interface applications thatlet users input commands through gestures, and orientation applicationswhich can automatically change the display from portrait to landscapewhen the mobile device is rotated. An accelerometer can be provided,e.g., by a micro-electromechanical system (MEMS) which is a tinymechanical device (of micrometer dimensions) built onto a semiconductorchip. Acceleration direction, as well as orientation, vibration, andshock can be sensed. The one or more processors 8312 further communicatewith a ringer/vibrator 8316, a user interface keypad/screen 8318, aspeaker 8320, a microphone 8322, a camera 8324, a light sensor 8326, anda temperature sensor 8328. The user interface keypad/screen may includea touch-sensitive screen display.

The one or more processors 8312 controls transmission and reception ofwireless signals. During a transmission mode, the one or more processors8312 provide voice signals from microphone 8322, or other data signals,to the RF transmitter/receiver 8306. The transmitter/receiver 8306transmits the signals through the antenna 8302. The ringer/vibrator 8316is used to signal an incoming call, text message, calendar reminder,alarm clock reminder, or other notification to the user. During areceiving mode, the RF transmitter/receiver 8306 receives a voice signalor data signal from a remote station through the antenna 8302. Areceived voice signal is provided to the speaker 8320 while otherreceived data signals are processed appropriately.

Additionally, a physical connector 8388 may be used to connect themobile device 8300 to an external power source, such as an AC adapter orpowered docking station, in order to recharge battery 8304. The physicalconnector 8388 may also be used as a data connection to an externalcomputing device. For example, the data connection may allow foroperations such as synchronizing mobile device data with the computingdata on another device.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousaspects of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularaspects only and is not intended to be limiting of the disclosure. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of anymeans or step plus function elements in the claims below are intended toinclude any disclosed structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present disclosure has been presentedfor purposes of illustration and description, but is not intended to beexhaustive or limited to the disclosure in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of thedisclosure. The aspects of the disclosure herein were chosen anddescribed in order to best explain the principles of the disclosure andthe practical application, and to enable others of ordinary skill in theart to understand the disclosure with various modifications as aresuited to the particular use contemplated.

For purposes of this document, each process associated with thedisclosed technology may be performed continuously and by one or morecomputing devices. Each step in a process may be performed by the sameor different computing devices as those used in other steps, and eachstep need not necessarily be performed by a single computing device.

For purposes of this document, reference in the specification to “anembodiment,” “one embodiment,” “some embodiments,” or “anotherembodiment” are used to described different embodiments and do notnecessarily refer to the same embodiment.

For purposes of this document, a connection can be a direct connectionor an indirect connection (e.g., via another part).

For purposes of this document, the term “set” of objects, refers to a“set” of one or more of the objects.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method, comprising: determining a baselinegroup mood classification associated with a plurality of people, thebaseline group mood classification corresponds with a first time period;transmitting an electronic message to a plurality of target addressesassociated with the plurality of people subsequent to the first timeperiod and prior to a second time period; determining a group moodclassification associated with the plurality of people in response totransmitting the electronic message, the determining a group moodclassification comprises determining a plurality of individual moodclassifications associated with the plurality of people reading theelectronic message during the second time period and determining a mostfrequent classification of the plurality of individual moodclassifications, wherein determining the plurality of individual moodclassifications comprises detecting that a first person correspondingwith a first email address of the plurality of target addresses hascaused the electronic message to be displayed and determining a firstindividual mood classification of the plurality of individual moodclassifications associated with the first person while the first personis reading the electronic message; detecting that the group moodclassification has deviated from the baseline group mood classificationby more than a threshold amount and that the deviation comprises apositive deviation or a negative deviation; transmitting the electronicmessage to a second set of addresses different from the plurality oftarget addresses in response to detecting that the group moodclassification has deviated from the baseline group mood classificationby more than the threshold amount and that the deviation comprises thepositive deviation; and preventing transmission of the electronicmessage to the second set of addresses in response to detecting that thegroup mood classification has deviated from the baseline group moodclassification by more than the threshold amount and that the deviationcomprises the negative deviation.
 2. The method of claim 1, wherein: thedetermining a plurality of individual mood classifications comprisesapplying facial expression and mood detection techniques to images ofthe plurality of people captured during the second time period; and thedetermining the plurality of individual mood classifications comprisesdetermining the first individual mood classification of the plurality ofindividual mood classifications associated with the first person whilethe first person is reading the electronic message at a first point intime and determining a second individual mood classification of theplurality of individual mood classifications associated with a secondperson while the second person is reading the electronic message at asecond point in time different from the first point in time.
 3. Themethod of claim 1, wherein: the detecting that the group moodclassification has deviated from the baseline group mood classificationcomprises determining that a first numerical value associated with thegroup mood classification is less than a second numerical valueassociated with the baseline group mood classification by more than thethreshold amount.
 4. The method of claim 1, wherein: the detecting thatthe group mood classification has deviated from the baseline group moodclassification comprises determining that a first numerical valueassociated with the group mood classification is more than a secondnumerical value associated with the baseline group mood classificationby more than the threshold amount.
 5. The method of claim 1, wherein:the determining a baseline group mood classification comprises applyingfacial expression and mood detection techniques to images of theplurality of people captured during the first time period.
 6. The methodof claim 1, wherein: the second time period is less than the first timeperiod.
 7. The method of claim 1, wherein: the determining a group moodclassification is performed by a server; and the detecting that thegroup mood classification has deviated is performed by the server.
 8. Asystem, comprising: a storage device configured to store a baselinegroup mood classification associated with a plurality of people, thebaseline group mood classification corresponds with a first time period;and a processor configured to transmit an electronic message to aplurality of target addresses associated with the plurality of peoplesubsequent to the first time period and prior to a second time periodand determine a group mood classification associated with the pluralityof people in response to transmitting the electronic message, theprocessor configured to determine a plurality of individual moodclassifications associated with the plurality of people asynchronouslyreading the electronic message during the second time period anddetermine a most frequent classification of the plurality of individualmood classifications, the processor configured to detect that a firstperson corresponding with a first email address of the plurality oftarget addresses has caused the electronic message to be displayed andidentify a first individual mood classification of the plurality ofindividual mood classifications associated with the first person whilethe first person is reading the electronic message, the processorconfigured to detect that the group mood classification has deviatedfrom the baseline group mood classification by more than a thresholdamount and that the deviation comprises a positive deviation or anegative deviation, the processor configured to transmit the electronicmessage to a second set of addresses different from the plurality oftarget addresses in response to detecting that the group moodclassification has deviated from the baseline group mood classificationby more than the threshold amount and that the deviation comprises thepositive deviation, the processor configured to prevent transmission ofthe electronic message to the second set of addresses in response todetecting that the group mood classification has deviated from thebaseline group mood classification by more than the threshold amount andthat the deviation comprises the negative deviation.
 9. The system ofclaim 8, wherein: the processor is configured to determine the groupmood classification by acquiring images of the plurality of peoplecaptured during the second time period and applying mood detectiontechniques to the images.
 10. The system of claim 8, wherein: theprocessor is configured to detect that the group mood classification hasdeviated from the baseline group mood classification by determining thata first numerical value associated with the group mood classification isless than a second numerical value associated with the baseline groupmood classification by more than the threshold amount.
 11. A computerprogram product, comprising: a non-transitory computer readable storagemedium having computer readable program code embodied therewith, thecomputer readable program code comprising: computer readable programcode configured to determine a baseline group mood classificationassociated with a plurality of people, the baseline group moodclassification corresponds with a first time period; computer readableprogram code configured to transmit an electronic message to a pluralityof target addresses associated with the plurality of people subsequentto the first time period and prior to a second time period; computerreadable program code configured to determine a group moodclassification associated with the plurality of people in response totransmitting the electronic message, the determining a group moodclassification comprises determining a plurality of individual moodclassifications associated with the plurality of people asynchronouslyreading the electronic message during the second time period anddetermining a most frequent classification of the plurality ofindividual mood classifications, the determining the plurality ofindividual mood classifications comprises detecting that a first personcorresponding with a first email address of the plurality of targetaddresses has caused the electronic message to be displayed andidentifying a first individual mood classification of the plurality ofindividual mood classifications associated with the first person whilethe first person is reading the electronic message; computer readableprogram code configured to detect that the group mood classification hasdeviated from the baseline group mood classification by more than athreshold amount and that the deviation comprises a positive deviationor a negative deviation; computer readable program code configured totransmit the electronic message to a second set of addresses differentfrom the plurality of target addresses in response to detecting that thegroup mood classification has deviated from the baseline group moodclassification by more than the threshold amount and that the deviationcomprises the positive deviation; and computer readable program codeconfigured to prevent transmission of the electronic message to thesecond set of addresses in response to detecting that the group moodclassification has deviated from the baseline group mood classificationby more than the threshold amount and that the deviation comprises thenegative deviation.
 12. The computer program product of claim 11,wherein: the determining the plurality of individual moodclassifications comprises applying facial expression and mood detectiontechniques to images of the plurality of people captured during thesecond time period.
 13. The computer program product of claim 11,wherein: the detecting that the group mood classification has deviatedfrom the baseline group mood classification comprises determining that afirst numerical value associated with the group mood classification isless than a second numerical value associated with the baseline groupmood classification by more than the threshold amount.
 14. The computerprogram product of claim 11, wherein: the second time period is lessthan the first time period.